Stock Forecasting Using Natural Language and Recurrent Network

Kumar, D (2020) Stock Forecasting Using Natural Language and Recurrent Network. In: 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), 7-8 Feb. 2020, Jaipur, India, India, pp. 36-40.

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Abstract

The factors affecting the stock price of a company include financial aspects, government policies, international policies, emerging news, which affects the investors and hence the market. Critical technical indicators are taken into account while proposing multi-level machine learning in this paper. Firstly, the sentiments of news articles analyzed using lexicon-based Natural Language Processing (NLP). The lexicon used is exclusively based on financial, social media. Each news article analyzed for aspect extraction and aspect-based sentiment analysis. Along with market news sentiments and the company's historical financial data, feature vector also includes critical technical analyses based on trend, momentum, and volatility. The future stock price movements forecast utilizes Long Short-Term Memory-Recursive Neural Network (LSTM-RNN) model. The results indicate that the discussed model performs well without requiring any data preprocessing, cycle analyses, or seasonality testing. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	cited By 0; Conference of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020 ; Conference Date: 7 February 2020 Through 8 February 2020; Conference Code:159907
Keywords:	Commerce; Engineering education; Financial markets; Internet of things; Investments; Learning algorithms; Machine learning; Sentiment analysis; Well testing, Data preprocessing; International policies; NAtural language processing; Recurrent networks; Recursive neural networks; Stock price movements; Technical analysis; Technical indicator, Long short-term memory
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	04 Nov 2020 11:25
Last Modified:	04 Nov 2020 11:25
URI:	http://eprints.iisc.ac.in/id/eprint/65869

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